Graph clustering
if(!file.exists("../analysis/filtered_IMM_DN_int_clus.rds")){
alldata = readRDS(file = "../analysis/filtered_IMM_DN_int.rds")
alldata@active.assay = "CCA"
alldata <- FindNeighbors(alldata, dims = 1:30, k.param = 350, prune.SNN = 1/15)
names(alldata@graphs)
pheatmap(alldata@graphs$CCA_nn[1:200, 1:200], col = c("white", "black"), border_color = "grey90",
legend = F, cluster_rows = F, cluster_cols = F, fontsize = 2)
# Clustering with louvain (algorithm 1)
for (res in c(0.1, 0.25, 0.5, 1, 1.5, 2)) {
alldata <- FindClusters(alldata, graph.name = "CCA_snn",
resolution = res, algorithm = 1)
}
saveRDS(alldata, file = "../analysis/filtered_IMM_DN_int_clus.rds")
}else{
alldata = readRDS(file = "../analysis/filtered_IMM_DN_int_clus.rds")
}
plot_grid(ncol = 2,
DimPlot(alldata, reduction = "umap", group.by = "CCA_snn_res.0.1",
label = TRUE) + NoLegend() +
ggtitle("louvain_0.1") + theme(legend.position = "bottom"),
DimPlot(alldata, reduction = "umap", group.by = "CCA_snn_res.0.25",
label = TRUE) + NoLegend() +
ggtitle("louvain_0.25") + theme(legend.position = "bottom"),
DimPlot(alldata, reduction = "umap", group.by = "CCA_snn_res.0.5",
label = TRUE) + NoLegend() +
ggtitle("louvain_0.5") + theme(legend.position = "bottom"),
DimPlot(alldata, reduction = "umap", group.by = "CCA_snn_res.1",
label = TRUE) + NoLegend() +
ggtitle("louvain_1") + theme(legend.position = "bottom"))
clustree(alldata@meta.data, prefix = "CCA_snn_res.")
QC, CCA louvain 0.25
table(alldata$CCA_snn_res.0.25)
VlnPlot(alldata, features = c("nFeature_CCA", "nCount_CCA", "percent_mito", "percent_ribo",
"S.Score", "G2M.Score"), group.by = "CCA_snn_res.0.25" ,
ncol = 3, pt.size = 0.1)
VlnPlot(alldata, features = c("nFeature_CCA", "nCount_CCA", "percent_mito", "percent_ribo",
"S.Score", "G2M.Score"), group.by = "CCA_snn_res.0.25" ,
ncol = 3, pt.size = 0)
#plot as proportion or percentage of cluster
ggplot(alldata@meta.data, aes(x=CCA_snn_res.0.25, fill=orig.ident)) + geom_bar(position = "fill")
#plot as proportion or percentage of cluster
ggplot(alldata@meta.data, aes(x=orig.ident, fill=CCA_snn_res.0.25)) + geom_bar(position = "fill")
ggplot(alldata@meta.data, aes(x=Treatment, fill=CCA_snn_res.0.25)) + geom_bar(position = "fill") + facet_wrap(~Type)
ggplot(alldata@meta.data, aes(fill=Type, x=CCA_snn_res.0.25)) + geom_bar(position = "fill")
DotPlot(alldata, features = c("Ptprc","Epcam"), group.by = "CCA_snn_res.0.25",
assay = "CCA") + coord_flip()
##
## 0 1 2 3 4 5 6 7 8 9 10 11 12
## 11520 7328 6802 5322 5179 4375 3126 2667 2616 2198 1918 1620 1546
## 13 14 15 16
## 1492 1312 567 289
QC, CCA louvain 0.5
table(alldata$CCA_snn_res.0.5)
VlnPlot(alldata, features = c("nFeature_CCA", "nCount_CCA", "percent_mito", "percent_ribo",
"S.Score", "G2M.Score"), group.by = "CCA_snn_res.0.5" ,
ncol = 3, pt.size = 0.1)
VlnPlot(alldata, features = c("nFeature_CCA", "nCount_CCA", "percent_mito", "percent_ribo",
"S.Score", "G2M.Score"), group.by = "CCA_snn_res.0.5" ,
ncol = 3, pt.size = 0)
#plot as proportion or percentage of cluster
ggplot(alldata@meta.data, aes(x=CCA_snn_res.0.5, fill=orig.ident)) + geom_bar(position = "fill")
#plot as proportion or percentage of cluster
ggplot(alldata@meta.data, aes(x=orig.ident, fill=CCA_snn_res.0.5)) + geom_bar(position = "fill")
ggplot(alldata@meta.data, aes(x=Treatment, fill=CCA_snn_res.0.5)) + geom_bar(position = "fill") + facet_wrap(~Type)
ggplot(alldata@meta.data, aes(fill=Type, x=CCA_snn_res.0.5)) + geom_bar(position = "fill")
DotPlot(alldata, features = c("Ptprc","Epcam"), group.by = "CCA_snn_res.0.5",
assay = "CCA") + coord_flip()
##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## 7323 7024 6791 5324 5179 4469 4375 3126 2667 2616 1920 1622 1563 1562 1498 1325
## 16 17 18
## 634 570 289
QC, CCA louvain 1
table(alldata$CCA_snn_res.1)
VlnPlot(alldata, features = c("nFeature_CCA", "nCount_CCA", "percent_mito", "percent_ribo",
"S.Score", "G2M.Score"), group.by = "CCA_snn_res.1" ,
ncol = 3, pt.size = 0.1)
VlnPlot(alldata, features = c("nFeature_CCA", "nCount_CCA", "percent_mito", "percent_ribo",
"S.Score", "G2M.Score"), group.by = "CCA_snn_res.1" ,
ncol = 3, pt.size = 0)
#plot as proportion or percentage of cluster
ggplot(alldata@meta.data, aes(x=CCA_snn_res.1, fill=orig.ident)) + geom_bar(position = "fill")
#plot as proportion or percentage of cluster
ggplot(alldata@meta.data, aes(x=orig.ident, fill=CCA_snn_res.1)) + geom_bar(position = "fill")
ggplot(alldata@meta.data, aes(x=Treatment, fill=CCA_snn_res.1)) + geom_bar(position = "fill") + facet_wrap(~Type)
ggplot(alldata@meta.data, aes(fill=Treatment, x=CCA_snn_res.1)) + geom_bar(position = "fill")
DotPlot(alldata, features = c("Ptprc","Epcam"), group.by = "CCA_snn_res.1",
assay = "CCA") + coord_flip()
##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## 6780 6745 6137 5325 5183 4480 4375 2670 2616 2411 1920 1627 1576 1563 1502 1341
## 16 17 18 19 20 21
## 857 704 637 570 569 289
sessionInfo()
## R version 4.0.5 (2021-03-31)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS/LAPACK: /Users/jenfra/miniconda3/envs/singlecell/lib/libopenblasp-r0.3.15.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] future_1.27.0 clustree_0.5.0 ggraph_2.0.5 rafalib_1.0.0
## [5] pheatmap_1.0.12 ggplot2_3.3.6 cowplot_1.1.1 sp_1.5-0
## [9] SeuratObject_4.1.0 Seurat_4.1.1
##
## loaded via a namespace (and not attached):
## [1] Rtsne_0.15 colorspace_2.0-3 deldir_1.0-6
## [4] ellipsis_0.3.2 ggridges_0.5.3 rstudioapi_0.13
## [7] spatstat.data_2.2-0 farver_2.1.0 leiden_0.3.9
## [10] listenv_0.8.0 graphlayouts_0.8.0 ggrepel_0.9.1
## [13] fansi_1.0.3 codetools_0.2-18 splines_4.0.5
## [16] cachem_1.0.6 knitr_1.37 polyclip_1.10-0
## [19] jsonlite_1.8.0 ica_1.0-2 cluster_2.1.2
## [22] png_0.1-7 rgeos_0.5-9 uwot_0.1.11
## [25] ggforce_0.3.3 shiny_1.7.2 sctransform_0.3.3
## [28] spatstat.sparse_2.1-1 compiler_4.0.5 httr_1.4.2
## [31] backports_1.4.1 assertthat_0.2.1 Matrix_1.4-1
## [34] fastmap_1.1.0 lazyeval_0.2.2 cli_3.3.0
## [37] tweenr_1.0.2 later_1.3.0 htmltools_0.5.3
## [40] tools_4.0.5 igraph_1.3.4 gtable_0.3.0
## [43] glue_1.6.2 RANN_2.6.1 reshape2_1.4.4
## [46] dplyr_1.0.9 Rcpp_1.0.9 scattermore_0.8
## [49] jquerylib_0.1.4 vctrs_0.4.1 nlme_3.1-155
## [52] progressr_0.10.0 lmtest_0.9-40 spatstat.random_2.2-0
## [55] xfun_0.31 stringr_1.4.0 globals_0.16.0
## [58] mime_0.12 miniUI_0.1.1.1 lifecycle_1.0.1
## [61] irlba_2.3.5 goftest_1.2-3 MASS_7.3-55
## [64] zoo_1.8-9 scales_1.1.1 tidygraph_1.2.0
## [67] spatstat.core_2.4-4 promises_1.2.0.1 spatstat.utils_2.3-1
## [70] parallel_4.0.5 RColorBrewer_1.1-2 yaml_2.3.5
## [73] reticulate_1.25 pbapply_1.5-0 gridExtra_2.3
## [76] sass_0.4.0 rpart_4.1.16 stringi_1.7.8
## [79] highr_0.9 checkmate_2.1.0 rlang_1.0.4
## [82] pkgconfig_2.0.3 matrixStats_0.62.0 evaluate_0.15
## [85] lattice_0.20-45 tensor_1.5 ROCR_1.0-11
## [88] purrr_0.3.4 labeling_0.4.2 patchwork_1.1.1
## [91] htmlwidgets_1.5.4 tidyselect_1.1.2 parallelly_1.32.1
## [94] RcppAnnoy_0.0.19 plyr_1.8.7 magrittr_2.0.3
## [97] bookdown_0.24 R6_2.5.1 generics_0.1.3
## [100] DBI_1.1.3 withr_2.5.0 mgcv_1.8-39
## [103] pillar_1.8.0 fitdistrplus_1.1-8 survival_3.3-1
## [106] abind_1.4-5 tibble_3.1.8 future.apply_1.8.1
## [109] crayon_1.5.1 KernSmooth_2.23-20 utf8_1.2.2
## [112] spatstat.geom_2.4-0 plotly_4.10.0 rmarkdown_2.13
## [115] viridis_0.6.2 grid_4.0.5 data.table_1.14.2
## [118] digest_0.6.29 xtable_1.8-4 tidyr_1.2.0
## [121] httpuv_1.6.5 munsell_0.5.0 viridisLite_0.4.0
## [124] bslib_0.4.0